In order to improve the stormwater regulation functions of urban greenways on the basis of literature research and case study the relationships between urban greenway and low impact development LID and green stormwate...In order to improve the stormwater regulation functions of urban greenways on the basis of literature research and case study the relationships between urban greenway and low impact development LID and green stormwater infrastructure GSI are analyzed. Then the classification system of urban greenways is proposed based on their stormwater regulation function and the suitable technical measures for stormwater management which can be used in different kinds of greenways are selected. According to China’s urban planning system the greenway planning method combined with the urban drainage system is developed and the design methods of the greenway stormwater system and individual stormwater facilities are put forward. The relationships between the greenway stormwater system and other systems are also analyzed in terms of stormwater inlet vertical design and overflow.Finally the waterfront greenway and street greenway demonstration projects in Jiaxing City which adopts the above concept and method are introduced. The results show that the reduction rates of annual total stormwater runoff and average total runoff contaminants TSS of the stormwater system are not less than 30% and 40% respectively.展开更多
Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN....Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.展开更多
基金The National Natural Science Foundation of China(No.51208020)
文摘In order to improve the stormwater regulation functions of urban greenways on the basis of literature research and case study the relationships between urban greenway and low impact development LID and green stormwater infrastructure GSI are analyzed. Then the classification system of urban greenways is proposed based on their stormwater regulation function and the suitable technical measures for stormwater management which can be used in different kinds of greenways are selected. According to China’s urban planning system the greenway planning method combined with the urban drainage system is developed and the design methods of the greenway stormwater system and individual stormwater facilities are put forward. The relationships between the greenway stormwater system and other systems are also analyzed in terms of stormwater inlet vertical design and overflow.Finally the waterfront greenway and street greenway demonstration projects in Jiaxing City which adopts the above concept and method are introduced. The results show that the reduction rates of annual total stormwater runoff and average total runoff contaminants TSS of the stormwater system are not less than 30% and 40% respectively.
基金The National Natural Science Foundation of China(No.50479017).
文摘Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.
